Risk-based control-to-range

ABSTRACT

Methods and systems are disclosed for determining a basal rate adjustment of insulin based on risk associated with a glucose state of a person with diabetes. A method may include detecting a glucose state of the person based on a received glucose measurement signal and determining a current risk metric associated with the detected glucose state. The method may include determining a current risk metric associated with the detected glucose state based on a weighted average of cumulative hazard values of return paths generated from a glucose state distribution around a detected glucose state. The method may include calculating an adjustment to a basal rate of a therapy delivery device based on the current risk metric associated with the detected glucose state and a reference risk metric associated with a reference glucose level.

TECHNICAL FIELD

The present invention generally relates to processing glucose datameasured from a person having diabetes and, in particular, forcontrolling adjustment of a temporary basal rate based on riskassociated with a glucose state of a person with diabetes.

BACKGROUND

Many people suffer from Type I or Type II diabetes, in which the bodydoes not properly regulate the blood glucose level. A continuous glucosemonitor (CGM) allows the interstitial glucose level of a patient withdiabetes to be measured on an ongoing basis, such as every few minutes.The timing and dosage of insulin to administer to the patient may bedetermined on the basis of measurements recorded by the CGM device.Glucose readings from CGM devices are displayed to the patient, and thepatient can inject insulin or consume meals to help control the glucoselevel. Insulin pumps can deliver precise insulin dosages on aprogrammable schedule which may be adjusted by the patient or healthcare provider.

Hazard metrics may be derived from glucose data for assessing a hazardto the diabetic person based on a detected glucose level. For example, aknown hazard metric includes the hazard function proposed in thefollowing paper: Kovatchev, B. P. et al., Symmetrization of the bloodglucose measurement scale and its applications, Diabetes Care, 1997, 20,1655-1658. The Kovatchev hazard function is defined by the equationh(g)=[1.509 (log(g)^(1.0804)−5.381)]², wherein g is the blood glucoseconcentration (in milligrams per deciliter or mg/dl) and h(g) is thecorresponding penalty value. The Kovatchev function provides a staticpenalty (i.e., hazard) value in that the penalty depends only on theglucose level. The minimum (zero) hazard occurs at 112.5 mg/dl. Thehazard with the glucose level approaching hypoglycemia risessignificantly faster than the hazard with the glucose level approachinghyperglycemia.

The Kovatchev hazard function fails to account for the rate of change ofthe glucose level as well as the uncertainty associated with themeasured glucose level. For example, a patient's hazard associated with100 mg/dl and a rapidly falling blood glucose level is likely greaterthan the patient's hazard associated with 100 mg/dl with a constantglucose rate of change. Further, measured glucose results may beinaccurate due to sensor noise, sensor malfunction, or detachment of thesensor.

Various approaches have been made to control the glucose levels ofdiabetic people based on CGM glucose data. One approach for limiting theoccurrence of hypoglycemic conditions includes an insulin pump shutoffalgorithm that completely shuts off the basal insulin if the CGM glucoselevel drops below a low glucose threshold, such as 50 to 70 mg/dl, andlater resumes the basal insulin after a few hours. However, this on/offapproach adversely requires the adverse condition of crossing the lowglucose threshold to occur before action is taken. Further, thisapproach does not take into account the speed with which the glucose iscrossing the threshold, which may be problematic for patients (e.g.,children, active individuals, etc.) with a high rate of glucose change.

Another approach is to alert the patient of predicted hypoglycemia, andthe patient then consumes an amount of carbohydrates and waits apredetermined time period. If the system still predicts hypoglycemia thepatient repeats the cycle until the system no longer predictshypoglycemia. However, this approach makes the assumption that thepatient is able to consume carbohydrates immediately upon being alertedof the predicted hypoglycemia. Further, the patient may overcorrect byconsuming too many carbs, possibly leading to weight gain or to trendingthe glucose levels towards hyperglycemia.

Accordingly, some embodiments of the present disclosure provide apredictive approach for adjusting a therapy basal rate by mapping therisk of the estimated glucose state to an adjustment of the basal ratebased on cumulative hazard values of return paths generated from aglucose state distribution around the estimated glucose state. Riskassociated with the glucose state is based on the blood glucose level,the rate of change of the blood glucose level, and the standarddeviations of the blood glucose level and rate of change. Further, someembodiments provide for adjusting the calculated risk for a glucosestate in response to a meal bolus, an insulin bolus, and/or other eventssuch as exercise, glucagon availability, and stress that may affect therisk of hypoglycemia or hyperglycemia.

SUMMARY

In one embodiment, a method of determining a basal rate adjustment ofinsulin based on risk associated with a glucose state of a person withdiabetes is provided. The method includes receiving, by at least onecomputing device, a signal representative of at least one glucosemeasurement. The method also includes detecting, by the at least onecomputing device, a glucose state of the person based on the signal, thedetected glucose state including a glucose level of the person and arate of change of the glucose level. Further, the method includesdetermining, by the at least one computing device, a current risk metricassociated with the detected glucose state based on a target glucosestate, the target glucose state being stored in memory accessible by theat least one computing device, the current risk metric indicating a riskof at least one of a hypoglycemic condition and a hyperglycemiccondition of the person. A return path is determined based on atransition from the current glucose state to the target glucose state,the return path comprising at least one intermediate glucose valueassociated with a return to the target glucose state. Further, acumulative hazard value of the return path is determined, the cumulativehazard value including a sum of the hazard values of the at least oneglucose value on the return path, each hazard value being indicative ofa hazard associated with the corresponding intermediate glucose value.Additionally, the current risk metric is determined based on a weightedaverage of cumulative hazard values of return paths generated from aglucose state distribution around the detected glucose state. The methodalso includes identifying, by the at least one computing device, areference glucose state and a reference risk metric associated with thereference glucose state; and calculating, by the at least one computingdevice, an adjustment to a basal rate of a therapy delivery device basedon the current risk metric associated with the detected glucose stateand the reference risk metric associated with the reference glucoselevel.

In another embodiment, blood glucose management device configured todetermine a basal rate adjustment based on risk associated with aglucose state of a person with diabetes is provided. The device includesa non-transitory computer-readable medium storing executableinstructions; and at least one processing device configured to executethe executable instructions such that, when executed by the at least oneprocessing device, the executable instructions cause the at least oneprocessing device to receive a signal representative of at least oneglucose measurement. The executable instructions also cause the at leastone processing device to detect a glucose state of the person based onthe signal, the detected glucose state including a glucose level of theperson and a rate of change of the glucose level. Additionally, theexecutable instructions cause the at least one processing device todetermine a current risk metric associated with the detected glucosestate based on a target glucose state, the target glucose state beingstored in memory accessible by the at least one computing device, thecurrent risk metric indicating a risk of at least one of a hypoglycemiccondition and a hyperglycemic condition of the person. A return path isdetermined based on a transition from the current glucose state to thetarget glucose state, the return path comprising at least oneintermediate glucose value associated with a return to the targetglucose state. A cumulative hazard value of the return path isdetermined, the cumulative hazard value including a sum of the hazardvalues of the at least one glucose value on the return path, each hazardvalue being indicative of a hazard associated with the correspondingintermediate glucose value. The current risk metric is determined basedon a weighted average of cumulative hazard values of return pathsgenerated from a glucose state distribution around the detected glucosestate. The executable instructions also cause the at least oneprocessing device to identify a reference glucose state and a referencerisk metric associated with the reference glucose state. Finally, theexecutable instructions also cause the at least one processing device tocalculate an adjustment to a basal rate of a therapy delivery devicebased on the current risk metric associated with the detected glucosestate and the reference risk metric associated with the referenceglucose level.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments set forth in the drawings are illustrative and exemplaryin nature and not intended to limit the inventions defined by theclaims. The following detailed description of the illustrativeembodiments can be understood when read in conjunction with thefollowing drawings, where like structure is indicated with likereference numerals and in which:

FIG. 1 illustrates a continuous glucose monitoring (CGM) systemaccording to one or more embodiments shown and described herein;

FIG. 2 illustrates an exemplary blood glucose management device, therapydelivery device, and glucose sensor of the CGM system of FIG. 2, theblood glucose management device including a bolus calculator module,control-to-range logic, hazard analysis logic, a recursive filter, andbasal rate adjustment logic;

FIG. 3 illustrates a graph plotting an exemplary CGM trace and anadjusted maximum allowed glucose following a meal event;

FIG. 4 illustrates a graph plotting periodic updates to the basal rate;

FIG. 5 illustrates a graph plotting a hazard function with exemplaryhyperglycemic aggressiveness and hyperglycemic shift adjustments;

FIG. 6 illustrates a graph plotting a hazard function with hypoglycemicshifts due to exercise or availability of glucagon;

FIG. 7 illustrates a graph plotting exemplary return paths to the targetglucose level;

FIG. 8A illustrates a hypoglycemic risk surface with an array of samplespositions corresponding to a glucose state distribution;

FIG. 8B illustrates exemplary return paths for the highlighted glucosestates of FIG. 8A;

FIG. 9 illustrates a graph providing a continuous basal multiplier andan incremental basal multiplier;

FIG. 10A illustrates a basal rate adjustment plot; and

FIG. 10B illustrates a basal rate adjustment plot of FIG. 10A with ahyperglycemic shift due to a recent meal or correction bolus.

DETAILED DESCRIPTION

The embodiments described herein generally relate to methods and systemsfor determining a basal rate adjustment of insulin in a continuousglucose monitoring system of a person with diabetes and, in particular,for determining a basal rate adjustment of insulin based on riskassociated with a glucose state of a person with diabetes. For thepurposes of defining the present disclosure, the “measured glucoseresults” are the glucose levels of the person as measured by the glucosesensor; the “actual glucose level” or “true glucose measurement” is theactual glucose level of the person.

Referring to FIG. 1, an exemplary continuous glucose monitoring (CGM)system 10 is illustrated for monitoring the glucose level of a personwith diabetes (PWD) 11. In particular, CGM system 10 is operative tocollect a measured glucose value at a predetermined, adjustableinterval, such as every one minute, five minutes, or at other suitableintervals. CGM system 10 illustratively includes a glucose sensor 16having a needle or probe 18 that is inserted under the skin 12 of theperson. The end of the needle 18 is positioned in interstitial fluid 14,such as blood or another bodily fluid, such that measurements taken byglucose sensor 16 are based on the level of glucose in interstitialfluid 14. Glucose sensor 16 is positioned adjacent the abdomen of theperson or at another suitable location. Furthermore, the glucose sensor16 may be periodically calibrated in order to improve its accuracy. Thisperiodic calibration may help correct for sensor drift due to sensordegradation and changes in the physiological condition of the sensorinsertion site. Glucose sensor 16 may comprise other components as well,including but not limited to a wireless transmitter 20 and an antenna22. Glucose sensor 16 may alternatively use other suitable devices fortaking measurements, such as, for example, a non-invasive device (e.g.,infrared light sensor). Upon taking a measurement, glucose sensor 16transmits the measured glucose value via a communication link 24 to acomputing device 26, illustratively a blood glucose (bG) managementdevice 26. The bG management device 26 may also be configured to storein memory 39 a plurality of measured glucose results received from theglucose sensor 16 over a period of time.

CGM system 10 further includes a therapy delivery device 31,illustratively an insulin infusion pump 31, for delivering therapy(e.g., insulin) to the person. Insulin pump 31 is in communication withmanagement device 26 via a communication link 35, and management device26 is able to communicate bolus and basal rate information to insulinpump 31. Insulin pump 31 includes a catheter 33 having a needle that isinserted through the skin 12 of the person 11 for injecting the insulin.Insulin pump 31 is illustratively positioned adjacent the abdomen of theperson or at another suitable location. Similar to glucose sensor 16,infusion pump 31 also includes a wireless transmitter and an antenna forcommunication with management device 26. Insulin pump 31 is operative todeliver basal insulin (e.g., small doses of insulin continuously orrepeatedly released at a basal rate) and bolus insulin (e.g., a surgedose of insulin, such as around a meal event, for example). The bolusinsulin may be delivered in response to a user input triggered by theuser, or in response to a command from management device 26. Similarly,the basal rate of the basal insulin is set based on user input or inresponse to a command from management device 26. Infusion pump 31 mayinclude a display for displaying pump data and a user interfaceproviding user controls. In an alternative embodiment, insulin pump 31and glucose sensor 16 may be provided as a single device worn by thepatient, and at least a portion of the logic provided by processor ormicrocontroller may reside on this single device. Bolus insulin may alsobe injected by other means, such as manually by the user via a needle.

In one embodiment, such a CGM system 10 is referred to as an artificialpancreas system that provides closed loop or semi-closed loop therapy tothe patient to approach or mimic the natural functions of a healthypancreas. In such a system, insulin doses are calculated based on theCGM readings from the glucose sensor 16 and are automatically deliveredto the patient based on the CGM reading. For example, if the CGMindicates that the user has a high blood glucose level or hyperglycemia,the system can calculate an insulin dose necessary to reduce the user'sblood glucose level below a threshold level or to a target level andautomatically deliver the dose. Alternatively, the system canautomatically suggest a change in therapy such as an increased insulinbasal rate or delivery of a bolus, but can require the user to acceptthe suggested change prior to delivery. If the CGM data indicates thatthe user has a low blood glucose level or hypoglycemia, the system can,for example, automatically reduce a basal rate, suggest to the user toreduce a basal rate, automatically deliver or suggest that the userinitiate the delivery of an amount of a substance such as, e.g., ahormone (glucagon) to raise the concentration of glucose in the blood,suggest that the user, e.g., ingest carbohydrates and/or automaticallytake other actions and/or make other suggestions as may be appropriateto address the hypoglycemic condition, singly or in any desiredcombination or sequence. In some embodiments, multiple medicaments canbe employed in such a system such as a first medicament, e.g., insulin,that lowers blood glucose levels and a second medicament, e.g.,glucagon, which raises blood glucose levels.

Communication links 24, 35 are illustratively wireless, such as a radiofrequency (“RF”) or other suitable wireless frequency, in which data andcontrols are transmitted via electromagnetic waves between sensor 16,therapy delivery device 31, and management device 26. Bluetooth® is oneexemplary type of wireless RF communication system that uses a frequencyof approximately 2.4 Gigahertz (GHz). Another exemplary type of wirelesscommunication scheme uses infrared light, such as the systems supportedby the Infrared Data Association® (IrDA®). Other suitable types ofwireless communication may be provided. Furthermore, each communicationlink 24, 35 may facilitate communication between multiple devices, suchas between glucose sensor 16, computing device 26, insulin pump 31, andother suitable devices or systems. Wired links may alternatively beprovided between devices of system 10, such as, for example, a wiredEthernet link. Other suitable public or proprietary wired or wirelesslinks may be used.

FIG. 2 illustrates an exemplary management device 26 of the CGM system10 of FIG. 2. Management device 26 includes at least one microprocessoror microcontroller 32 that executes software and/or firmware code storedin memory 39 of management device 26. The software/firmware codecontains instructions that, when executed by the microcontroller 32 ofmanagement device 26, causes management device 26 to perform thefunctions described herein. Management device 26 may alternativelyinclude one or more application-specific integrated circuits (ASICs),field-programmable gate arrays (FPGAs), digital signal processors(DSPs), hardwired logic, or combinations thereof. While managementdevice 26 is illustratively a glucose monitor 26, other suitablemanagement devices 26 may be provided, such as, for example, desktopcomputers, laptop computers, computer servers, personal data assistants(“PDA”), smart phones, cellular devices, tablet computers, infusionpumps, an integrated device including a glucose measurement engine and aPDA or cell phone, etc. Although management device 26 is illustrated asa single management device 26, multiple computing devices may be usedtogether to perform the functions of management device 26 describedherein.

Memory 39 is any suitable computer readable medium that is accessible bymicrocontroller 32. Memory 39 may be a single storage device or multiplestorage devices, may be located internally or externally to managementdevice 26, and may include both volatile and non-volatile media.Further, memory 39 may include one or both of removable andnon-removable media. Exemplary memory 39 includes random-access memory(RAM), read-only memory (ROM), electrically erasable programmable ROM(EEPROM), flash memory, CD-ROM, Digital Versatile Disk (DVD) or otheroptical disk storage, a magnetic storage device, or any other suitablemedium which is configured to store data and which is accessible bymanagement device 26.

The microcontroller 32 may also include additional programming to allowthe microcontroller 32 to learn user preferences and/or usercharacteristics and/or user history data. This information can beutilized to implement changes in use, suggestions based on detectedtrends, such as, weight gain or loss. The microcontroller 32 can alsoinclude programming that allows the device 26 to generate reports, suchas reports based upon user history, compliance, trending, and/or othersuch data. Additionally insulin infusion pump 31 embodiments of thedisclosure may include a “power off” or “suspend” function forsuspending one or more functions of the device 26, such as, suspending adelivery protocol, and/or for powering off the device 26 or the deliverymechanism thereof. For some embodiments, two or more microcontrollers 32may be used for controller functions of insulin infusion pump 31,including a high power controller and a low power controller used tomaintain programming and pumping functions in low power mode, in orderto save battery life.

Management device 26 further includes a communication device 41operatively coupled to microcontroller 32. Communication device 41includes any suitable wireless and/or wired communication moduleoperative to transmit and receive data and controls over communicationlinks 24, 35 between device 26 and glucose sensor 16 and insulin pump31. In one embodiment, communication device 41 includes an antenna 30(FIG. 1) for receiving and/or transmitting data wirelessly overcommunication links 24, 35. Management device 26 stores in memory 39measured glucose results and other data received from glucose sensor 16and/or insulin pump 31 via communication device 41.

Management device 26 includes one or more user input device(s) 34 forreceiving user input. Input device(s) 34 may include pushbuttons,switches, a mouse pointer, keyboard, touchscreen, or any other suitableinput device. Display 28 is operatively coupled to microcontroller 32,and may comprise any suitable display or monitor technology (e.g.,liquid crystal display, etc.) configured to display information providedby microcontroller 32 to a user. Microcontroller 32 is configured totransmit to display 28 information related to the detected glucose stateof the person, the risk associated with the glucose state, and basalrate and bolus information. The glucose state may include the estimatedglucose level and the estimated rate-of-change of the glucose level, aswell as an estimate of the quality or uncertainty of the estimatedglucose level. Moreover, the displayed information may include warnings,alerts, etc. regarding whether the estimated or predicted glucose levelof the person is hypoglycemic or hyperglycemic. For example, a warningmay be issued if the person's glucose level falls below (or is predictedto fall below) a predetermined hypoglycemic threshold, such as 50 to 70milligrams of glucose per deciliter of blood (mg/dl). Management device26 may also be configured to tactilely communicate information orwarnings to the person, such as for example by vibrating.

In one embodiment, management device 26 is in communication with aremote computing device (not shown), such as at a caregiver's facilityor a location accessible by a caregiver, and data (e.g., glucose data orother physiological information) is transferred between them. In thisembodiment, management device 26 and the remote device are configured totransfer physiological information through a data connection such as,for example, via the Internet, cellular communications, or the physicaltransfer of a memory device such as a diskette, USB key, compact disc,or other portable memory device.

Microcontroller 32 also includes control-to-range logic 44. Acontrol-to-range system reduces the likelihood of a hypoglycemic eventor a hyperglycemic event by adjusting insulin dosing only if the PWD's11 glucose level approaches the low or high glucose thresholds.

Microcontroller 32 includes hazard analysis logic 40 that calculatestarget return paths from a plurality of initial glucose states to atarget glucose state based on cumulative hazard values. The targetglucose state is illustratively an optimal or ideal glucose state havingno associated hazard or risk, such as a glucose level of 112.5 mg/dl anda glucose rate-of-change of zero, although any suitable target glucosestate may be identified. Each target return path is comprised of aplurality of intermediate glucose states that are to be encounteredduring a transition from the initial glucose state to the target glucosestate. Cumulative penalty values associated with the target return pathsare stored in memory 76 that may be used as a lookup table. Calculationof cumulative penalty values is discussed infra.

In some embodiments, inaccurate glucose measurements may result frommalfunction and/or noise associated with glucose sensor 24. As such,hazard analysis logic 40 also analyzes the probability of accuracy ofthe detected glucose state provided with glucose sensor 24. Hazardanalysis logic 40 may use any suitable probability analysis tool todetermine the probability of accuracy of a measured glucose result, suchas a hidden Markov model. Based on the determined probability ofaccuracy, hazard analysis logic 40 estimates the glucose level and theglucose rate of change of the person using a recursive filter 42. Inparticular, recursive filter 42, such as a Kalman filter, for example,weights the detected glucose state, including the glucose level and rateof change, with the determined probability of glucose sensor accuracy.Based on the probability of glucose sensor accuracy, recursive filter 42calculates an uncertainty measure of the estimated glucose state. Theuncertainty measure is indicative of the quality of the estimatedglucose state. For a series of detected glucose states, the uncertaintyfor each state may vary.

Microcontroller 32 of FIG. 2 further includes a bolus calculator module48 that calculates bolus recommendations and a maximum allowed glucoselevel of a user which may be displayed to a user via display 28.Management device 26 maintains a record in memory 39 of historical datafor the user accumulated over time leading up to the current time. Thehistorical data includes blood glucose history, prescription data, priorbolus recommendations, prior administered boluses, prior basal rates,glucose sensitivity factors for the user's sensitivity to insulin andcarbohydrates, blood glucose responses to prior boluses and meal events,other user health and medical data, and the time stamp of each event anddata recordation. The history data includes patient recorded informationsuch as meal events, amount of carbohydrates consumed, confirmations ofbolus deliveries, medications, exercise events, periods of stress,physiological events, manual insulin injections, and other healthevents, entered via user inputs 34. Bolus calculator module 48 uses thehistorical data to more accurately and efficiently determine therecommended insulin bolus and/or carbohydrate amount.

The bolus calculator module 48 determines a recommended bolus, such asan insulin correction bolus or a meal bolus, particular to the userbased on the current glucose state, the history data, and user input. Asuggested meal bolus (e.g., carbohydrate amount) may be in response to adetected or predicted hypoglycemic condition. A suggested correctionbolus of insulin may be in response to the detected glucose exceedingthe maximum allowable glucose level. The actual amount of carbohydratesconsumed and the actual amount of insulin administered may be confirmedby the user as information entered via user inputs 34 and recorded inmemory 39 with other history data. The recommended bolus may bedisplayed on display 28.

Referring to FIG. 3, an exemplary CGM trace 100 is illustrated, whereinthe x-axis represents time in minutes and the y-axis represents glucosein mg/dl. CGM trace 100 comprises a series of detected glucose levelsmeasured over a period. In the illustrated embodiment, CGM trace 100represents filtered glucose levels, i.e., glucose levels that areestimated based on the measured glucose levels weighted with theprobably of sensor accuracy. A most recent estimated glucose level 110has an associated negative rate of change indicated with arrow 112.Bolus calculator module 48 determines the target glucose level 102 and atarget range of glucose levels indicated with an upper glucose limit 104and a lower glucose limit 106. For illustrative purposes, target glucoselevel 102 is 110 mg/dl, upper glucose limit 104 is 140 mg/dl, and lowerglucose limit 106 is 80 mg/dl, although other suitable values may beprovided. Bolus calculator module 48 may determine target glucose level102 and limits 104, 106 based at least in part on the user's historydata described herein. Management device 26 uses the trending glucosedata of CGM trace 100 to recommend corrective action to move the bloodglucose towards the target glucose level 102. The target glucose level102 of FIG. 3 corresponds to the maximum allowed glucose before time t₁and after time t₂, i.e., when there has not been any recent meals orcorrection boluses. Between times t₁ and t₂, the maximum allowed glucoseis adjusted based on a meal event 114 or other suitable events.

At time t₁, meal event 114 occurs when the user consumes a meal andenters carbohydrate data into management device 26 indicating the amountof carbohydrates consumed with the meal. In some instances, an insulinbolus is administered at about the time of the meal event 114 to offsetthe expected increase in glucose levels resulting from the meal. Boluscalculator module 48 determines a projected glucose level rise and aduration of the glucose rise based on the carbohydrates consumed, theinsulin correction bolus (if administered), and the user's historicaldata related to glucose swings following meals and insulin injections.Based on the projected glucose rise, bolus calculator module 48determines an allowed rise value 124, an offset time value 126, and anacting time value 122. The allowed rise value 124 may be based on otherevents, such as a glucagon injection, exercise, sleep, driving, or timeof day, for example.

The allowed rise value 124 is the amount by which the glucose level ofthe user may be allowed to increase with respect to the target glucoselevel 102 as a result of the carbohydrate intake and insulin bolus. Insome embodiments, the allowed rise value 124 is the combination of acorrection delta glucose value 130 resulting from an insulin bolus and ameal rise value 132 resulting from the meal event 114. The correctiondelta glucose value 130 is the difference between the current glucoselevel and the target glucose level 102 at the time of the insulin bolusto allow time for the glucose level to decrease following insulin. Asillustrated, the allowed rise value 124 is constant (see line 118) for afirst predetermined amount of time after the meal and insulinadministration, i.e., offset time 126, and then decreases linearly (seeslope 120) following the offset time 126. The total time that the mealand insulin dose have an effect on the bG levels of a patient is theacting time 122. FIG. 3 illustrates a trapezoid-shaped graph 116 of theallowed rise value 124 accounting for the effect of a dose of insulinand meal event.

The maximum allowed glucose increases based on allowed rise value 124and follows plot 116 of FIG. 3. As such, bolus calculator module 48expands the range of allowable glucose levels after a meal event for theduration of the acting time 122 according to plot 116. The allowed risevalue 124 illustratively has an initial height of 50 mg/dl, but couldhave other suitable heights based on the meal size, the insulin, and theuser's typical reactions to boluses from the historical data. In someembodiments, for meal events above a threshold amount of carbohydrates,the meal rise value 132 is fixed. As one example, the offset time 126 isabout two hours, and the acting time 122 is about three to five hours,depending on the user, the meal size, and the insulin bolus.

Referring again to FIG. 2, management device 26 further includes basalrate adjustment logic 50 operative to calculate and adjust a basal ratebased on the current glucose state and the risk associated with thecurrent glucose state. Management device 26 transmits an adjustment tothe basal rate in a control signal to insulin pump 31 via communicationlink 35, and insulin pump 31 adjusts the current insulin basal ratebased on the adjustment. Alternatively, the adjusted basal rate may bedisplayed to the user, and the user manually adjusts the basal rate ofinsulin pump 31. In one or more embodiment, the adjustment is a percentreduction to the initial, unadjusted or nominal basal rate based on arisk of hypoglycemia or a percent increase to the initial, unadjusted ornominal basal rate based on risk of hyperglycemic conditions.

The basal rate adjustment logic 50 determines whether the basal rate isto be adjusted. If an adjusted basal rate is proper, basal rateadjustment logic 50 calculates an adjusted basal rate and managementdevice 26 transmits a control signal to insulin pump 31 to cause insulinpump 31 to deliver insulin at the adjusted basal rate. Alternatively,management device 26 may display the adjusted basal rate to the user toprompt the user for manual adjustment of the insulin pump 31. In someembodiments, the implementation of the adjusted basal rate may beoverridden by the user via manual control of the insulin pump 31.

A basal rate multiplier adjustment is determined from a glucosemeasurement. In one or more embodiments, the basal rate multiplier ischanged at a fixed interval, for example 15 minutes. The glucose valueand glucose rate-of-change are used to predict the glucose value at themidpoint of the next fixed interval when calculating a new basal ratemultiplier. FIG. 4 shows an example with a fixed interval of length d soat time t₁ the glucose value and trend at g₁ is used to predict thevalue at time {circumflex over (t)}₁. This value, ĝ₁, is then used tocalculate the basal rate multiplier that will be used between time t₁and t₂.

Determination of the basal rate multiplier for implementation beginswith estimating the current glucose state. The full glucose stateincludes the glucose level, glucose rate-of-change, and a covariancematrix indicating the spread of the glucose level and glucoserate-of-change. These values are provided by the recursive filter 42. Ifthe noise of a sensor is close to constant, then the glucose state canbe reduced to just the glucose and rate-of-change.

In determining adjustments to the basal rate, it is assumed that the CGMcontroller receives a measurement every minute (or other periodicperiod), but communicates with the insulin pump less frequently. Once atemporary basal rate (TBR) for a period has been transmitted to thepump, the algorithm waits at least d minutes before another TBR commandis sent. In at least one embodiment d is equal to 15 minutes such thatthe TBR is updated on a periodic 15 minute basis. In furtherembodiments, d is equal to 10 minutes, 5 minutes, 2 minutes, or 1minute, for example. It will be appreciated that d may be adjusted basedon the individual needs of the PwD.

As previously discussed, microcontroller 32 includes hazard analysislogic 40 that calculates target return paths from a plurality of initialglucose states to a target glucose state based on cumulative hazardvalues. FIGS. 5 and 6 illustrate an exemplary hazard function 80 forcalculating a hazard value for a given glucose level ultimately utilizedin determination of the cumulative hazard value. The hazard function 80is defined by the following equation:

$\begin{matrix}{{h(g)}_{hyper} = {\max\left( {{\alpha_{hyper} \cdot {\alpha\left( {{\log\left( {\max\left( {{g - {\Delta\; g_{hyper}} - {\max\left( {{\Delta\; g_{hypo}},0} \right)}},1} \right)} \right)}^{c} - \beta} \right)}},0} \right)}} & (1) \\{\mspace{79mu}{{h(g)}_{hypo} = {\min\left( {{\alpha\left( {{\log\left( {\max\left( {{g - {\Delta\; g_{hypo}}},1} \right)} \right)}^{c} - \beta} \right)},0} \right)}}} & (2) \\{\mspace{79mu}{{h(g)} = \left\{ \begin{matrix}h_{MAX} & {{{{if}\mspace{14mu} g} - {\Delta\; g_{hyper}} - {\max\left( {{\Delta\; g_{hypo}},0} \right)}} \geq g_{MAX}} \\h_{MIN} & {{{{if}\mspace{14mu} g} - {\Delta\; g_{hypo}}} \leq g_{MIN}} \\{h(g)}_{hyper} & {{{if}\mspace{14mu}{h(g)}_{hypo}} \geq 0} \\{h(g)}_{hypo} & {{{if}\mspace{14mu}{h(g)}_{hypo}} < 0}\end{matrix} \right.}} & (3)\end{matrix}$where g is the blood glucose value (mg/dl) shown on the x-axis, h(g) isthe corresponding hazard value shown on the y-axis, Δg_(hyper) is ahyperglycemic shift, Δg_(hyper) is a hypoglycemic shift, h_(MAX) is amaximum hazard, h_(MIN) is a minimum hazard, α_(hyper) is thehyperglycemic control aggressiveness, and α, β, and c are processvariables. In the illustrated embodiment, the variables α, β, and c aredefined as follows: α=1.509, β=5.381, and c=1.084. g_(MAX) is a glucosevalue above which no additional incremental hazard is calculated aboveh_(MAX) and similarly g_(MIN) is a glucose value below which noadditional incremental hazard is calculated above h_(MIN). Test cases ofhazard functions for a hyperclycemic range (h(g)_(hyper)) and ahypoglycemic range (h(g)_(hypo)) are generated. The h(g) functiondetermines if h_(MAX), h_(MIN), h(g)_(hyper), or h(g)_(hypo) should beimplemented as the final hazard value for the tested blood glucosevalue.

Implementation of g_(MAX) and g_(MIN) in the determination of h_(MAX)and h_(MIN) respectively prevent excessively positive or negative hazardvalues for extreme blood glucose values. In one or more embodimentsg_(MAX) is set at 600 mg/dl and h_(MAX) is the h(g)_(hyper) associatedwith g_(MAX). Similarly, in one or more embodiments g_(MIN) is set at 10mg/dl and h_(MIN) is the h(g)_(hypo) associated with g_(MIN). As such,if g exceeds g_(MAX) or drops below g_(MIN), the hazard value associatedwith the blood glucose value is prevented from exceeding the rangedefined by h_(MAX) and h_(MIN).

Patients with diabetes exhibit varying degrees of insulin sensitivity.As such the parameter α_(hyper) provides functionality to adjust theaggressiveness of the hyperglycemic hazard function (h(g)_(hyper)) toaccount for the varying insulin sensitivities. With reference to FIG. 5,a nominal hazard function 80 is shown along with a hazard function withreduced α_(hyper) 82.

With reference to FIG. 5, Δg_(hyper) shifts the hazard function 80 inthe hyperglycemic region (positive hazard values) to account for arecent meal or correction bolus. Hyper shift hazard function 84illustrates a shift in the hazard function after a previous meal orcorrection bolus.

With reference to FIG. 6, Δg_(hypo) shifts the hazard function toaccount for recent exercise, availability of glucagon, or an excessivecorrection bolus, for example. For safety, the hyperglycemic hazardregion that is associated with an increase in insulin is never shiftedto the left. When glucagon is present the hypoglycemic hazard region isshifted to the left 86 because the glucagon accounts for a portion ofthe hypoglycemic hazard. The hyperglycemic hazard is not shifted in suchinstance because insulin administration should not be increased due toglucagon. In the case of exercise, for example, the hypoglycemic hazardis increased and the curve is shifted to the right 88. In this case theentire hazard curve is shifted.

The cumulative hazard value of a return path from the current glucosestate to the target glucose state is calculated by summing the hazardvalues of the glucose values on the path between the current glucosestate and the target glucose state. The path is constrained by limitingthe maximum allowed glucose acceleration. Additionally, the target isassumed to have a rate-of-change of zero as once the target glucosestate is reached it is desired to remain at the target glucose state andnot oscillate above and below the target glucose state.

The return path of minimum risk between the glucose state and the targetis the fastest path. This return path uses the maximum allowed glucoseaccelerations, both positive and negative glucose accelerations, toreturn to the target glucose state. The closed form solution to thereturn path generation is composed of a time period with one extreme ofthe allowed glucose accelerations followed by the opposite extreme.

If a positive hypoglycemic shift is being used then the hypoglycemicshift must be added to the target glucose to get the shifted glucosetarget. This is necessary to correctly shift the hypoglycemic risk asthe glucose target represents the blood glucose level where the hazardshifts from positive (hyperglycemic) to negative (hypoglycemic). Theadjustment of the target glucose to the shifted glucose target isdefined by the following equation:ĝ _(t) =g _(t)+max(Δg _(hypo),0)  (4)where ĝ_(t) is the shifted glucose target, g_(t) is the nominal glucosetarget, and Δg_(hypo) is the hypoglycemic shift. The maximum function inequation 4 prevents a negative hypoglycemic shift from being added tothe target glucose and instead uses a hypoglycemic shift of zeroresulting in ĝ_(t) and g_(t) being equal.

As an initial matter, the generalized form of the return path must bedetermined. The return path may have an initial positive glucoseacceleration followed by a negative glucose acceleration or may have aninitial negative glucose acceleration followed by a positive glucoseacceleration. The generalized form of the return path may be determinedby solving which of equation 5 and equation 6, presented infra, returnsa real number solution.

$\begin{matrix}{T^{\pm} = {t_{1}^{\pm} + t_{2}^{\pm}}} & (5) \\{T^{\mp} = {t_{1}^{\mp} + {t_{2}^{\mp}\mspace{14mu}{where}}}} & (6) \\{{t_{1}^{\pm} = \frac{\sqrt{{{\overset{¨}{g}}_{n}\left( {{\overset{¨}{g}}_{p} - {\overset{¨}{g}}_{n}} \right)}\left( {{- {\overset{.}{g}}^{2}} + {2\;{\overset{¨}{g}}_{p}} - {2\;{\hat{g}}_{t}{\overset{¨}{g}}_{p}}} \right)} - {\overset{.}{g}\;{\overset{¨}{g}}_{p}} + {\overset{.}{g}\;{\overset{¨}{g}}_{n}}}{{\overset{¨}{g}}_{p}\left( {{\overset{¨}{g}}_{p} - {\overset{¨}{g}}_{n}} \right)}},} & (7) \\{{t_{2}^{\pm} = \frac{\overset{.}{g} + {{\overset{¨}{g}}_{p}t_{1}^{\pm}}}{- {\overset{¨}{g}}_{n}}},} & (8) \\{{t_{1}^{\mp} = \frac{\sqrt{{{\overset{¨}{g}}_{p}\left( {{\overset{¨}{g}}_{n} - {\overset{¨}{g}}_{p}} \right)}\left( {{- {\overset{.}{g}}^{2}} + {2\;{\overset{¨}{g}}_{n}} - {2\;{\hat{g}}_{t}{\overset{¨}{g}}_{n}}} \right)} - {\overset{.}{g}\;{\overset{¨}{g}}_{n}} + {\overset{.}{g}\;{\overset{¨}{g}}_{p}}}{{\overset{¨}{g}}_{n}\left( {{\overset{¨}{g}}_{n} - {\overset{¨}{g}}_{p}} \right)}},} & (9) \\{{t_{2}^{\mp} = \frac{\overset{.}{g} + {{\overset{¨}{g}}_{n}t_{1}^{\mp}}}{- {\overset{¨}{g}}_{p}}},} & (10)\end{matrix}$ġ is the rate of change of the glucose level, {umlaut over (g)}_(p) isthe maximum positive glucose acceleration, {umlaut over (g)}_(n) is themaximum negative glucose acceleration, and ĝ_(t) is the shifted glucosetarget from equation 4. If equation 5 returns a real number for T^(∓)and both t₁ ^(∓) and t₂ ^(∓) are greater than or equal to zero, thereturn path utilizes a positive acceleration first and a negativeacceleration second. Conversely, if equation 6 returns a real number forT^(∓) and both t₁ ^(∓) and t₂ ^(∓) are greater than or equal to zero,the return path utilizes a negative acceleration first and a positiveacceleration second.

Once the generalized form of the return path is determined, thecumulative hazard value of the return path may be calculated. When thereturn path utilizes a positive acceleration first, the cumulativehazard value is defined by the following equation:

$\begin{matrix}{{h\left( {g,\overset{.}{g}} \right)} = {{\sum\limits_{t = 0}^{t_{1}^{\pm}}{h\left( {g + {\overset{.}{g}\; t} + {\frac{1}{2}{\overset{¨}{g}}_{p}t^{2}}} \right)}} + {\sum\limits_{t = 0}^{t_{2}^{\pm}}{h\left( {{\hat{g}}_{t} + {\frac{1}{2}{\overset{¨}{g}}_{n}t^{2}}} \right)}}}} & (11)\end{matrix}$and when the return path utilizes a negative acceleration first, thecumulative hazard value is defined by the following equation:

$\begin{matrix}{{h\left( {g,\overset{.}{g}} \right)} = {{\sum\limits_{t = 0}^{t_{1}^{\mp}}{h\left( {g + {\overset{.}{g}\; t} + {\frac{1}{2}{\overset{¨}{g}}_{n}t^{2}}} \right)}} + {\sum\limits_{t = 0}^{t_{2}^{\mp}}{{h\left( {{\hat{g}}_{t} + {\frac{1}{2}{\overset{¨}{g}}_{p}t^{2}}} \right)}.}}}} & (12)\end{matrix}$

It should be appreciated that return paths that encounter more extremeglucose values will tend to have a higher cumulative hazard value as thehazard value for each time point is higher as illustrated in FIGS. 5 and6. For example, a blood glucose value of 225 mg/dl would have a higherhazard value than a blood glucose value of 120 mg/dl at the same glucoserate-of-change. Also, paths that take a longer time to return to thetarget glucose state will tend to have a higher hazard value. A path mayrequire longer returning to the target glucose state as a result ofinitial glucose rate-of-change or extreme glucose values. With referenceto FIG. 7, exemplary return paths for a broad range of initial glucosevalues where the initial rate-of-change is zero are provided. The timeto the target glucose state in FIG. 7 ranges from about 20 minutes toalmost 180 minutes. This amplifies the differences in cumulative hazardvalues for the initial glucose states. Calculating the cumulative hazardvalue allows for the comparison of glucose states with different glucosevalues and rates-of-change. Often a glucose value closer to the targetglucose value has a higher hazard value than a more distant glucosevalue if the glucose rate-of-change is more extreme.

The cumulative hazard value provides the hazard for a specific returnpath from the current glucose state to the target glucose state.However, there are uncertainties in CGM blood glucose measurements fromglucose sensor 16. As such, the true blood glucose measurement may varyfrom the blood glucose determined by the glucose sensor 16 and thespecific calculated cumulative hazard value may be inaccurate withregards to the actual return path. To account for the variability in thetrue return path, a current risk metric is determined which accounts forvariance in the CGM blood glucose measurements.

To calculate the current risk metric, a predicted glucose state at anintermediate point of the CTR period is initially determined. In variousembodiments, the intermediate point of the CTR period is the truemidpoint (½ of the CTR period), ¼ of the CTR period, ⅓ of the CTRperiod, ⅔ the CTR period, or ¾ of the CTR period. In an embodiment, theCTR is typically updated every 15 minutes resulting in the midpointbeing 7.5 minutes into the 15 minute sampling interval. For short timehorizons a linear prediction performs as well or better than morecomplicated models, so a linear prediction is used for simplicity. Therate-of-change in the glucose level is assumed to remain constant overthe 7.5 min window in determining the predicted blood glucose level atthe midpoint of the 15 minute sampling interval. As such, the predictedglucose level is defined by the following equation:ĝ=g+ġτ  (13)where g is the initial measured blood glucose level, ġ is the initialrate-of-change of the glucose level, and τ is the prediction timemeasured from the beginning of the CTR period. The predicted glucosestate is thus [ĝ, ġ].

Subsequently, a glucose state distribution around the predicted glucosestate is determined. Similarly, a glucose state distribution around thecurrent glucose state may also be determined. The samples for theglucose state distribution are selected based on the standard deviationof the distribution in the g and ġ directions. Generation of the glucosestate distribution samples is defined by the following equations:

$\begin{matrix}{G_{s} = \left\lbrack {{g - {2\sigma_{g}}},{g - {2\sigma_{g}} + \frac{4\sigma_{g}}{k}},{g - {2\sigma_{g}} + {2\frac{4\sigma_{g}}{k}}},{g - {2\sigma_{g}} + {3\frac{4\;\sigma_{g}}{k}}},\ldots\mspace{14mu},{g - {2\sigma_{g}} + {k\frac{4\sigma_{g}}{k}}}} \right\rbrack} & (14) \\{{\overset{.}{G}}_{s} = \left\lbrack {{\overset{.}{g} - {2\sigma_{\overset{.}{g}}}},{\overset{.}{g} - {2\sigma_{\overset{.}{g}}} + \frac{4\sigma_{\overset{.}{g}}}{n}},{\overset{.}{g} - {2\sigma_{\overset{.}{g}}} + {2\frac{4\sigma_{\overset{.}{g}}}{n}}},{\overset{.}{g} - {2\sigma_{\overset{.}{g}}} + {3\frac{4\;\sigma_{\overset{.}{g}}}{n}}},\ldots\mspace{14mu},{\overset{.}{g} - {2\sigma_{\overset{.}{g}}} + {n\frac{4\sigma_{\overset{.}{g}}}{n}}}} \right\rbrack} & (15)\end{matrix}$where G_(s) is the distribution of glucose values, Ġ_(s) is thedistribution of glucose rates of change, g is the glucose value for thecurrent risk metric, ġ is the rate of change of the glucose level forthe current risk metric, σ_(g) is the standard deviation of g, σ_(ġ) isthe standard deviation of ġ, k is the number of divisions of G_(s), andn is the number of divisions of Ġ_(s). It will be appreciated that g mayrepresented the current glucose level or the predicted glucose level ifthe glucose state distribution is desired for the current glucose stateor the predicted glucose state respectively. Equation 14 and equation 15provide a distribution of samples ranging within two standard deviationsof g and ġ. In at least one embodiment, the sampled values for g areselected by dividing the range bounded by two standard deviations by 10and the sampled values for ġ are selected by dividing the range boundedby two standard deviations by 8 such that k=10 and n=8 respectivly.Other sampling ranges and frequencies may also be used such as 3standard deviations.

The current risk metric is determined based on a weighted average of thecumulative hazard values of the return paths generated from each of thesampled glucose states. Specifically, the risk is calculated bydetermining the weighted average of the cumulative hazard values at eachcombination of points in G_(s) and Ġ_(s) and weighting them by amultivariate exponential function w(g_(s), ġ_(s)). The current riskmetric is defined by the following equation:

$\begin{matrix}{r = \frac{\Sigma_{G_{s}}\Sigma_{{\overset{.}{G}}_{s}}{h\left( {g_{s},{\overset{.}{g}}_{s}} \right)}{w\left( {g_{s},{\overset{.}{g}}_{s}} \right)}}{\Sigma_{G_{s}}\Sigma_{{\overset{.}{G}}_{s}}{w\left( {g_{s},{\overset{.}{g}}_{s}} \right)}}} & (16)\end{matrix}$where r is the current risk metric,

$\begin{matrix}{{{w\left( {g_{s},{\overset{.}{g}}_{s}} \right)} = {\exp\left( {{- {\frac{1}{2}\left\lbrack {g_{s} - {g\mspace{20mu}{\overset{.}{g}}_{s}} - \overset{.}{g}} \right\rbrack}}{P_{g}^{- 1}\begin{bmatrix}{g_{s} - g} \\{{\overset{.}{g}}_{s} - \overset{.}{g}}\end{bmatrix}}} \right)}},} & (17)\end{matrix}$G_(s) is the distribution of glucose values and Ġ_(s) is thedistribution of glucose rates of change determined from the glucosestate distribution around the detected glucose state, h(g_(s),ġ_(s)) isthe cumulative hazard value of the return path at each glucose state, gis the glucose value for the current risk metric, ġ is the rate ofchange of the glucose level for the current risk metric,

$\begin{matrix}{P_{g} = {\begin{bmatrix}\sigma_{g}^{2} & {\sigma_{g}\sigma_{\overset{.}{g}}} \\{\sigma_{\overset{.}{g}}\sigma_{g}} & \sigma_{\overset{.}{g}}^{2}\end{bmatrix}.}} & (18)\end{matrix}$σ_(g) is the standard deviation of g, and σ_(ġ) is the standarddeviation of ġ. The weighting of the cumulative hazard values results insamples closest to the measured glucose state receiving the largestweight in the final current risk metric calculation.

With reference to FIGS. 8A and 8B, determination of the current riskmetric is visually displayed. FIG. 8A illustrates the 99 glucose statesgenerated in an 11×9 matrix when k=10 and n=8 overlaid onto ahypoglycemic risk surface. The return paths for the 9 highlightedsamples from FIG. 8A are also highlighted in FIG. 8B. The weightedaverage of the cumulative hazard values for the return paths for theentire grouping of the 99 glucose states provides the current riskmetric.

The final basal multiplier for each CTR period is determined utilizingthe current risk metric. The current risk metric is first converted to abasal multiplier value between 0 and TBR_(MAX). TBR_(MAX) is the maximumpercentage for a temporary basal rate (TBR). In at least one embodiment,the TBR_(MAX) defaults to 250%. In further embodiments, the TBR_(MAX) islower or higher than 250% and is adjusted to tune the control anddetermination for hypo-adverse individuals. The basal multiplier valueis defined by the following equation:

$\begin{matrix}{{{BM}(r)} = \left\{ \begin{matrix}{\frac{r - r_{0\%}}{- r_{0\%}},} & {r > r_{0\%}} \\{0,} & {r \leq r_{0\%}}\end{matrix} \right.} & (19)\end{matrix}$where BM(r) is the basal multiplier value, r is the current risk metric,and r_(0%) is a reference risk metric. In one or more embodiments, thereference risk metric is a glucose state linked to complete basalshutoff. For example, complete basal shutoff may occur at 70 mg/dl suchthat when the blood glucose level is below 70 mg/dl no basal insulin isprovided. The basal multiplier value may be provided as a continuousfunction as the current risk metric varies. However, before providingthe adjusted basal rate to the therapy delivery device 31 it isconverted to the nearest TBR increment (TBR_(inc)) to provide anincremental basal rate multiplier (BM_(inc)). The incremental basal ratemultiplier is defined by the following equation:

$\begin{matrix}{{BM}_{inc} = {{\min\left( {{\max\left( {{{{floor}\left( \frac{{BM}(r)}{{TBR}_{inc}} \right)}{TBR}_{inc}},0} \right)},{TBR}_{MAX}} \right)}.}} & (20)\end{matrix}$With reference to FIG. 9, exemplary continuous basal multiplier valuesand incremental basal rate multipliers with a TBR_(inc) of 10% and theimplemented floor function are illustrated.

In another embodiment, basal multipliers greater than a threshold(BM_(bolus)) are delivered as a single bolus. The threshold could be100%, 110%, or 130%. In these cases the extra insulin (I_(TBR)) thatwould be delivered in the next period of d minutes is calculated usingthe anticipated basal rate (I_(BasalRate)) for the period and theduration of the period (d). The extra insulin is then delivered as asingle bolus and the basal rate multiplier is set to the threshold(BM_(bolus)).

$\begin{matrix}{I_{TBR} = {\left( {{BM}_{inc} - {BM}_{bolus}} \right)I_{BasalRate}\frac{d}{60}}} & (21)\end{matrix}$

As previously discussed, if the PwD has had a recent meal or correctionbolus, then a shift is applied to the hyperglycemic side of the hazardfunction 80. This reduces the calculated hyperglycemic risk since thereis insulin in the subcutaneous compartment to account for a portion ofthe hyperglycemic risk. With reference to FIGS. 10A and 10B, theresulting shift in the basal rate adjustment from the initial shiftapplied to the hyperglycemic side of the hazard function 80 isillustrated. FIG. 10A provides an exemplary basal rate adjustmentprofile with the curve passing through a glucose of approximately 115mg/dl and a rate-of-change of 0 mg/dl/min dividing basal rates above andbelow 100%; the curves below are basal rates below 100% and the curvesabove are basal rates above 100%. Similarly, FIG. 10B provides anexemplary basal rate adjustment profile with the hyperglycemic shiftadded. The lone curve passing through a glucose of approximately 140mg/dl and a rate-of-change of 0 mg/dl/min divides basal rates above andbelow 100%.

For some PwDs the max allowed TBR (TBR_(MAX)) should be set to a valuelower than 250% or the default setting for TBR_(MAX). These individualsare characterized by having a large glucose correction equivalent oftheir basal rate (G_(br)). This is calculated by multiplying the hourlybasal rate (BR) by the insulin sensitivity (IS). For example anindividual with a nominal basal rate of 0.9 IU/hr and an insulinsensitivity of 50 mg/dl/IU would have a glucose correction equivalent of45 mg/dl. PwD with a G_(br) above a threshold (G_(brT)) could benefitfrom a lowered TBR_(MAX). In one or more embodiments, the G_(brT) is setat 150 mg/dl. It will be appreciated that the G_(brT) may be set atvalues above or below 150 mg/dl as specific PwD circumstances warrant. Atemporary basal rate limit (TBR_(limit)) to provide a reduced TBR_(MAX)is defined by the following equation:

$\begin{matrix}{{TBR}_{limit} = {{\min\left( {{TBR}_{MAX},{\frac{G_{brT}}{{BR}*{IS}}*{TBR}_{MAX}}} \right)}.}} & (22)\end{matrix}$

Similarly to the incremental basal rate multiplier, the temporary basalrate limit may be incremented to the closest TBR increment. TheTBR_(limit) is incremented to the closest TBR increment as defined bythe following equation:

$\begin{matrix}{{TBR}_{{inc}\mspace{11mu}{limit}} = {{\min\left( {{\max\left( {{{{round}\left( \frac{{TBR}_{limit}}{10} \right)}*{TBR}_{inc}},100} \right)},250} \right)}.}} & (23)\end{matrix}$

The glucose correction equivalent was calculated for 30 simulated PwDs.The simulated subjects numbered 21 and 24 showed an oscillating behaviorwhen their insulin sensitivity was increased. In this scenario the basalrate was increased by a factor of 1.5 for subject number 24 to inducehypoglycemia and the CTR algorithm was turned on to mitigate theeffects. Simulations were repeated with different values for the maxallowed TBR value ranging from 125% to 250%. The lower values for themax allowed TBR value have a lower magnitude of the oscillationsdemonstrating the benefit of implementing a TBR_(limit) for PwD with aG_(br) above the G_(brT).

For further and alternative descriptions for determining the basal rateadjustment, see U.S. patent application Ser. No. 14/229,016, filed onMar. 28, 2015, entitled “System and Method for Adjusting Therapy Basedon Risk Associated with a Glucose State,” the entire disclosure of whichis incorporated by reference herein. For further description ofcalculating the target return paths and calculating risk metrics, seeU.S. patent application Ser. No. 13/645,198, filed on Oct. 4, 2012,entitled “System and Method for Assessing Risk Associated with a GlucoseState,” the entire disclosure of which is incorporated by referenceherein. For further description of the probability analysis tool, therecursive filter, the uncertainty calculation, and other probability andrisk analysis functionalities of computing device 66, see U.S. patentapplication Ser. No. 12/693,701, filed on Jan. 26, 2010, entitled“Methods and Systems for Processing Glucose Data Measured from a PersonHaving Diabetes,” and U.S. patent application Ser. No. 12/818,795, filedon Jun. 18, 2010, entitled “Insulin Optimization Systems and TestingMethods with Adjusted Exit Criterion Accounting for System NoiseAssociated with Biomarkers,” the entire disclosures of which areincorporated by reference herein. For further description of the boluscalculator module 88, see U.S. patent application Ser. No. 13/593,557,filed on Aug. 24, 2012, entitled “Handheld Diabetes Management Devicewith Bolus Calculator,” and U.S. patent application Ser. No. 13/593,575,filed on Aug. 24, 2012, entitled “Insulin Pump and Methods for Operatingthe Insulin Pump,” the entire disclosures of which are incorporated byreference herein.

It should now be understood that the methods and systems describedherein may be used to estimate the glucose level of a person havingdiabetes and utilize a control-to-range algorithm to adjust the glucoselevel of a person having diabetes. Furthermore, the methods and systemsdescribed herein may also be used to determine adjustments to the basalrate of insulin administration to the PwD. The methods described hereinmay be stored on a computer-readable medium which hascomputer-executable instructions for performing the methods. Suchcomputer-readable media may include compact discs, hard drives, thumbdrives, random-access memory, dynamic random-access memory, flashmemory, and so forth.

It is noted that recitations herein of a component of the presentdisclosure being “configured” in a particular way, “configured” toembody a particular property, or function in a particular manner, arestructural recitations, as opposed to recitations of intended use. Morespecifically, the references herein to the manner in which a componentis “configured” denotes an existing physical condition of the componentand, as such, is to be taken as a definite recitation of the structuralcharacteristics of the component.

While particular embodiments and aspects of the present invention havebeen illustrated and described herein, various other changes andmodifications may be made without departing from the spirit and scope ofthe invention. Moreover, although various inventive aspects have beendescribed herein, such aspects need not be utilized in combination. Itis therefore intended that the appended claims cover all such changesand modifications that are within the scope of this invention.

What is claimed is:
 1. A method of determining and administering a basalrate adjustment of insulin based on risk associated with a glucose stateof a person with diabetes, the method comprising: receiving, by at leastone computing device, a signal representative of at least one glucosemeasurement; detecting, by the at least one computing device, a glucosestate of the person based on the signal, the detected glucose stateincluding a glucose level of the person and a rate of change of theglucose level; determining, by the at least one computing device, acurrent risk metric associated with the detected glucose state based ona target glucose state, the target glucose state being stored in memoryaccessible by the at least one computing device, the current risk metricindicating a risk of at least one of a hypoglycemic condition and ahyperglycemic condition of the person, wherein a return path isdetermined based on a transition from the current glucose state to thetarget glucose state, the return path comprising at least oneintermediate glucose value associated with a return to the targetglucose state, wherein a cumulative hazard value of the return path isdetermined, the cumulative hazard value including a sum of the hazardvalues of the at least one glucose value on the return path, each hazardvalue being indicative of a hazard associated with the correspondingintermediate glucose value, wherein the current risk metric isdetermined based on a weighted average of cumulative hazard values ofreturn paths generated from a glucose state distribution around thedetected glucose state; identifying, by the at least one computingdevice, a reference glucose state and a reference risk metric associatedwith the reference glucose state; calculating, by the at least onecomputing device, an adjustment to a basal rate of a therapy deliverydevice based on the current risk metric associated with the detectedglucose state and the reference risk metric associated with thereference glucose level; transmitting a control signal to instruct thetherapy delivery device to adjust the basal rate based on the calculatedadjustment; and administering the adjusted basal rate to the person withdiabetes.
 2. The method of claim 1, wherein the calculating comprisesmapping the current risk metric to a percent reduction of the basal ratebased on the reference risk metric.
 3. The method of claim 2, whereinthe reference glucose state includes a glucose level corresponding to ahypoglycemic condition.
 4. The method of claim 1, further comprisingdisplaying to a user, on a graphical user interface, graphical datarepresentative of the calculated adjustment to the basal rate.
 5. Themethod of claim 1, wherein the therapy delivery device includes aninsulin pump for delivering insulin to the person with diabetes, and thetherapy delivery device is in communication with the at least onecomputing device for receiving the calculated adjustment of the basalrate.
 6. The method of claim 1, wherein the hazard value for each of thehazard values of the at least one glucose value on the return path isdetermined by the at least one computing device in accordance withh(g)_(hyper) = max (α_(hyper) ⋅ α(log (max (g − Δ g_(hyper) − max (Δ g_(hypo), 0), 1))^(c) − β), 0),  h(g)_(hypo) = min (α(log (max (g − Δ g_(hypo), 1))^(c) − β), 0), and$\mspace{20mu}{{h(g)} = \left\{ \begin{matrix}h_{MAX} & {{{{if}\mspace{14mu} g} - {\Delta\; g_{hyper}} - {\max\left( {{\Delta\; g_{hypo}},0} \right)}} \geq g_{MAX}} \\h_{MIN} & {{{{if}\mspace{14mu} g} - {\Delta\; g_{hypo}}} \leq g_{MIN}} \\{h(g)}_{hyper} & {{{if}\mspace{14mu}{h(g)}_{hypo}} \geq 0} \\{h(g)}_{hypo} & {{{if}\mspace{14mu}{h(g)}_{hypo}} < 0}\end{matrix} \right.}$ where g is the glucose value, Δg_(hyper) is ahyperglycemic shift, Δg_(hypo) is a hypoglycemic shift, h_(MAX) is amaximum hazard, g_(MAX) is a glucose value above which no additionalincremental hazard is calculated above h_(MAX), h_(MIN) is a minimumhazard, g_(MIN) is a glucose value below which no additional incrementalhazard is calculated above h_(MIN), α_(hyper) is the hyperglycemiccontrol aggressiveness, and α, β, and c are process variables.
 7. Themethod of claim 1, further comprising, prior to determining thecumulative hazard value of the return path, identifying, by the at leastone computing device, a shifted glucose target for the person to accountfor a positively shifted hypoglycemic risk in accordance withĝ _(t) =g _(t)+max(Δg _(hypo),0) where ĝ_(t) is the shifted glucosetarget, g_(t) is a nominal glucose target, and Δg_(hypo) is ahypoglycemic shift.
 8. The method of claim 7, wherein, if T^(±)=t₁^(±)+t₂ ^(±) is a real number, the cumulative hazard value of the returnpath is determined by the at least one computing device according to${h\left( {g,\overset{.}{g}} \right)} = {{\sum\limits_{t = 0}^{t_{1}^{\pm}}{h\left( {g + {\overset{.}{g}\; t} + {\frac{1}{2}{\overset{¨}{g}}_{p}t^{2}}} \right)}} + {\sum\limits_{t = 0}^{t_{2}^{\pm}}{h\left( {{\hat{g}}_{t} + {\frac{1}{2}{\overset{¨}{g}}_{n}t^{2}}} \right)}}}$where${t_{1}^{\pm} = \frac{\sqrt{{{\overset{¨}{g}}_{n}\left( {{\overset{¨}{g}}_{p} - {\overset{¨}{g}}_{n}} \right)}\left( {{- {\overset{.}{g}}^{2}} + {2{\overset{¨}{g}}_{p}} - {2{\hat{g}}_{t}{\overset{¨}{g}}_{p}}} \right)} - {\overset{.}{g}\;{\overset{¨}{g}}_{p}} + {\overset{.}{g}\;{\overset{¨}{g}}_{n}}}{{\overset{¨}{g}}_{p}\left( {{\overset{¨}{g}}_{p} - {\overset{¨}{g}}_{n}} \right)}},{t_{2}^{\pm} = \frac{\overset{.}{g} + {{\overset{¨}{g}}_{p}t_{1}^{\pm}}}{- {\overset{¨}{g}}_{n}}},$ġ is the rate of change of the glucose level, {umlaut over (g)}_(p) isthe maximum positive glucose acceleration, and {umlaut over (g)}_(n) isthe maximum negative glucose acceleration.
 9. The method of claim 7,wherein, if T^(∓)=t₁ ^(∓)+t₂ ^(∓) is a real number, the cumulativehazard value of the return path is determined by the at least onecomputing device according to${h\left( {g,\overset{.}{g}} \right)} = {{\sum\limits_{t = 0}^{t_{1}^{\mp}}{h\left( {g + {\overset{.}{g}\; t} + {\frac{1}{2}{\overset{¨}{g}}_{n}t^{2}}} \right)}} + {\sum\limits_{t = 0}^{t_{2}^{\mp}}{h\left( {{\hat{g}}_{t} + {\frac{1}{2}{\overset{¨}{g}}_{p}t^{2}}} \right)}}}$where${t_{1}^{\mp} = \frac{\sqrt{{{\overset{¨}{g}}_{p}\left( {{\overset{¨}{g}}_{n} - {\overset{¨}{g}}_{p}} \right)}\left( {{- {\overset{.}{g}}^{2}} + {2{\overset{¨}{g}}_{n}} - {2{\hat{g}}_{t}{\overset{¨}{g}}_{\; n}}} \right)} - {\overset{.}{g}\;{\overset{¨}{g}}_{n}} + {\overset{.}{g}\;{\overset{¨}{g}}_{p}}}{{\overset{¨}{g}}_{n}\left( {{\overset{¨}{g}}_{n} - {\overset{¨}{g}}_{p}} \right)}},{t_{2}^{\mp} = \frac{\overset{.}{g} + {{\overset{¨}{g}}_{n}t_{1}^{\mp}}}{- {\overset{¨}{g}}_{p}}},$ġ is the rate of change of the glucose level, {umlaut over (g)}_(p) isthe maximum positive glucose acceleration, and {umlaut over (g)}_(n) isthe maximum negative glucose acceleration.
 10. The method of claim 1,wherein the glucose state distribution is determined by the at least onecomputing device according to$G_{s} = {\left\lbrack {{g - {2\sigma_{g}}},{g - {2\sigma_{g}} + \frac{4\;\sigma_{g}}{k}},{g - {2\sigma_{g}} + {2\frac{4\;\sigma_{g}}{k}}},{g - {2\sigma_{g}} + {3\frac{4\;\sigma_{g}}{k}}},\ldots\mspace{14mu},{g - {2\sigma_{g}} + {k\frac{4\;\sigma_{g}}{k}}}} \right\rbrack\mspace{14mu}{and}}$${\overset{.}{G}}_{s} = \left\lbrack {{\overset{.}{g} - {2\sigma_{\overset{.}{g}}}},{\overset{.}{g} - {2\sigma_{\overset{.}{g}}} + \frac{4\;\sigma_{\overset{.}{g}}}{n}},{\overset{.}{g} - {2\sigma_{\overset{.}{g}}} + {2\frac{4\;\sigma_{\overset{.}{g}}}{n}}},{\overset{.}{g} - {2\sigma_{\overset{.}{g}}} + {3\frac{4\;\sigma_{\overset{.}{g}}}{n}}},\ldots\mspace{14mu},{\overset{.}{g} - {2\sigma_{\overset{.}{g}}} + {n\frac{4\;\sigma_{\overset{.}{g}}}{n}}}} \right\rbrack$where G_(s) is the distribution of glucose values, Ġ_(s) is thedistribution of glucose rates of change, g is the glucose value for thecurrent risk metric, ġ is the rate of change of the glucose level forthe current risk metric, σ_(g) is the standard deviation of g, σ_(ġ) isthe standard deviation of ġ, k is the number of divisions of G_(s), andn is the number of divisions of Ġ_(s).
 11. The method of claim 10,wherein k=10 and n=8.
 12. The method of claim 1, wherein the currentrisk metric is determined by the at least one computing device accordingto$r = \frac{\Sigma_{G_{s}}\Sigma_{{\overset{.}{G}}_{s}}{h\left( {g_{s},{\overset{.}{g}}_{s}} \right)}{w\left( {g_{s},{\overset{.}{g}}_{s}} \right)}}{\Sigma_{G_{S}}\Sigma_{{\overset{.}{G}}_{S}}{w\left( {g_{s},{\overset{.}{g}}_{s}} \right)}}$where r is the current risk metric,${{w\left( {g_{s},{\overset{.}{g}}_{s}} \right)} = {\exp\left( {{- {\frac{1}{2}\left\lbrack {g_{s} - {g\mspace{25mu}{\overset{.}{g}}_{s}} - g} \right\rbrack}}{P_{g}^{- 1}\begin{bmatrix}{g_{s} - g} \\{{\overset{.}{g}}_{s} - \overset{.}{g}}\end{bmatrix}}} \right)}},$ G_(s) is the distribution of glucose valuesand Ġ_(s) is the distribution of glucose rates of change determined fromthe glucose state distribution around the detected glucose state,h(g_(s), ġ_(s)) is the cumulative hazard value of the return path ateach glucose state, g is the glucose value for the current risk metric,ġ is the rate of change of the glucose level for the current riskmetric, ${P_{g} = \begin{bmatrix}\sigma_{g}^{2} & {\sigma_{g}\sigma_{\overset{.}{g}}} \\{\sigma_{\overset{.}{g}}\sigma_{g}} & \sigma_{\overset{.}{g}}^{2}\end{bmatrix}},$ σ_(g) is the standard deviation of g, and σ_(ġ) is thestandard deviation of ġ.
 13. The method of claim 2, wherein a basalmultiplier value is determined by the by the at least one computingdevice according to ${{BM}(r)} = \left\{ \begin{matrix}{\frac{r - r_{0\%}}{- r_{0\%}},} & {r > r_{0\%}} \\{0,} & {r \leq r_{0\%}}\end{matrix} \right.$ where BM(r) is the basal multiplier value, r isthe current risk metric, and r_(0%) is the reference risk metric. 14.The method of claim 13, wherein r_(0%) is the risk metric at a glucosestate linked to complete basal shutoff.
 15. The method of claim 14,wherein a temporary basal rate is determined for transmission to thetherapy delivery device is determined by the at least one computingdevice according to${BM}_{inc} = {\min\left( {{\max\left( {{{{floor}\left( \frac{{BM}(r)}{{TBR}_{inc}} \right)}{TBR}_{inc}},0} \right)}{TBR}_{MAX}} \right)}$where TBR_(inc) is the sizing of a temporary basal rate multiplieradjustment increment and TBR_(MAX) is the maximum temporary basal ratemultiplier.
 16. The method of claim 15, wherein a temporary basal ratemultiplier limit accounting for insulin sensitivity of the person isdetermined by the at least one computing device according to${TBR}_{limit} = {\min\left( {{TBR}_{MAX},{\frac{G_{brT}}{{BR}*{IS}}*{TBR}_{MAX}}} \right)}$where TBR_(limit) is the temporary basal rate multiplier limit, G_(brT)is a glucose correction equivalent threshold, BR is the nominal basalrate, and IS is the insulin sensitivity of the person.
 17. The method ofclaim 16, wherein the TBR_(MAX) is 250% and the G_(brT) is 150 mg/dl.18. The method of claim 6, wherein Δg_(hyper) and Δg_(hypo) are adjustedbased on detection of an over-correction bolus.
 19. A blood glucosemanagement device configured to determine a basal rate adjustment basedon risk associated with a glucose state of a person with diabetes, thedevice comprising: a non-transitory computer-readable medium storingexecutable instructions; and at least one processing device configuredto execute the executable instructions such that, when executed by theat least one processing device, the executable instructions cause the atleast one processing device to: receive a signal representative of atleast one glucose measurement; detect a glucose state of the personbased on the signal, the detected glucose state including a glucoselevel of the person and a rate of change of the glucose level; determinea current risk metric associated with the detected glucose state basedon a target glucose state, the target glucose state being stored inmemory accessible by the at least one computing device, the current riskmetric indicating a risk of at least one of a hypoglycemic condition anda hyperglycemic condition of the person, wherein a return path isdetermined based on a transition from the current glucose state to thetarget glucose state, the return path comprising at least oneintermediate glucose value associated with a return to the targetglucose state, wherein a cumulative hazard value of the return path isdetermined, the cumulative hazard value including a sum of the hazardvalues of the at least one glucose value on the return path, each hazardvalue being indicative of a hazard associated with the correspondingintermediate glucose value, wherein the current risk metric isdetermined based on a weighted average of cumulative hazard values ofreturn paths generated from a glucose state distribution around thedetected glucose state; identify a reference glucose state and areference risk metric associated with the reference glucose state;calculate an adjustment to a basal rate of a therapy delivery devicebased on the current risk metric associated with the detected glucosestate and the reference risk metric associated with the referenceglucose level; transmit a control signal to instruct the therapydelivery device to adjust the basal rate based on the calculatedadjustment; and administer the adjusted basal rate to the person withdiabetes.